TY - GEN
T1 - Identifying and tracking sentiments and topics from social media texts during natural disasters
AU - Yang, Min
AU - Mei, Jincheng
AU - Ji, Heng
AU - Zhao, Wei
AU - Zhao, Zhou
AU - Chen, Xiaojun
N1 - Heng Ji’s work was supported by U.S. DARPA LORELEI Program No. HR0011-15-C-0115. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
PY - 2017
Y1 - 2017
N2 - We study the problem of identifying the topics and sentiments and tracking their shifts from social media texts in different geographical regions during emergencies and disasters. We propose a location-based dynamic sentiment-topic model (LDST) which can jointly model topic, sentiment, time and Geolocation information.
AB - We study the problem of identifying the topics and sentiments and tracking their shifts from social media texts in different geographical regions during emergencies and disasters. We propose a location-based dynamic sentiment-topic model (LDST) which can jointly model topic, sentiment, time and Geolocation information.
UR - https://www.scopus.com/pages/publications/85063083298
UR - https://www.scopus.com/pages/publications/85063083298#tab=citedBy
U2 - 10.18653/v1/d17-1055
DO - 10.18653/v1/d17-1055
M3 - Conference contribution
AN - SCOPUS:85063083298
T3 - EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 527
EP - 533
BT - EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PB - Association for Computational Linguistics (ACL)
T2 - 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017
Y2 - 9 September 2017 through 11 September 2017
ER -